Duplicate Row Detection in Pandas DataFrames: A Subtle yet Powerful Technique

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In this article, we will delve into the world of duplicate row detection in Pandas DataFrames. Specifically, we’ll explore how to identify duplicate rows based on a percentage threshold for a subset of columns. We’ll also discuss the limitations of Pandas’ built-in duplicated() function and provide a workaround using clever indexing.

Background

Pandas is a powerful library used for data manipulation and analysis in Python. Its DataFrames are two-dimensional tables with columns of potentially different types. One of the most common use cases for DataFrames is data cleaning, where you need to identify and remove duplicate rows or handle missing values.

The duplicated() function is a convenient way to detect duplicate rows, but it has its limitations. For example, when used on all columns (df.duplicated()) without any specific column filtering, it can be computationally expensive for large DataFrames with many columns. Moreover, the duplicated() function returns only the first occurrence of each duplicate row, which might not always be what you want.

The Problem: Duplicate Row Detection with a Percentage Threshold

In our example, we have a DataFrame with more than 100 columns and suspect that it has duplicate rows. We can rule out using df.duplicated() on all columns because of its limitations. Instead, we want to identify duplicate rows based on a percentage threshold for a subset of columns.

Let’s assume we want to detect duplicates when at least 80% of the columns have the same values for a given row. This is not as straightforward as using duplicated(), but we can achieve this using clever indexing and Pandas’ built-in functions.

Solution: Using Clever Indexing and GroupBy

To solve this problem, we’ll use a combination of Pandas’ groupby() function and clever indexing. Here’s the step-by-step solution:

Select a subset of columns: Choose the columns you want to consider for duplicate row detection. For example, let’s say we want to consider only the first 20 columns (df[:, :20]).
Group by selected columns and calculate percentage similarity: Use groupby() to group the DataFrame by the selected columns. Then, calculate the percentage similarity between each group using the following formula:
```
(sum(x == y) / num_elements) * 100
```
Here, x and y represent the values in two different rows, and num_elements is the number of elements in the respective column.
Apply the percentage threshold: Set a percentage threshold (e.g., 80%) to determine whether a row should be considered a duplicate.
Filter duplicates: Use groupby() again with the same columns and calculate the similarity. This time, filter out rows where the percentage similarity is above or equal to the threshold.

Code Implementation

Here’s the code implementation of our solution:

import pandas as pd

# Sample DataFrame with duplicate rows
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Select a subset of columns (first 20 columns)
subset_cols = df.columns[:20]

# Group by selected columns and calculate percentage similarity
grouped_df = df.groupby(subset_cols).apply(lambda x: x.apply(lambda row: (x[x == row].sum(axis=0) / len(x)) * 100, axis=1))

# Apply the percentage threshold (80%)
threshold = 80

# Filter duplicates based on percentage similarity
duplicates = grouped_df[grouped_df.apply(lambda x: x >= threshold)]

print(duplicates)

Explanation and Example Use Cases

In this example, we’ve identified duplicate rows in the df DataFrame by considering only the first 20 columns. The groupby() function groups the DataFrame by these columns, calculates the percentage similarity between each group, and filters out rows where the similarity is above or equal to the threshold (80%).

This solution can be applied to various use cases, such as:

Data quality checks: Identify duplicate rows in a dataset to detect errors or inconsistencies.
Data cleaning: Remove duplicate rows from a DataFrame before performing further analysis or processing.
Machine learning: Handle duplicate samples in a dataset by identifying and removing them.

Conclusion

In conclusion, detecting duplicate rows based on a percentage threshold for a subset of columns is not as straightforward as using duplicated(), but it can be achieved using clever indexing and Pandas’ built-in functions. By selecting a subset of columns, grouping the DataFrame, calculating percentage similarity, applying a threshold, and filtering duplicates, we can identify duplicate rows in our dataset.

This technique can be applied to various use cases, including data quality checks, data cleaning, and machine learning. With this solution, you can improve the accuracy and reliability of your analysis by handling duplicate samples effectively.

Last modified on 2025-04-25